Harnessing Machine Learning for Advanced A/V Analysis and Detection

A/V analysis and detection are some of machine learning’s most practical applications. Many tools apply these capabilities to text-based data or network traffic, but audio and video use cases are also worth noting.

Applications of A/V Analysis and Detection

Identifying and interpreting A/V signals is useful beyond media sectors like film and music. Here’s a look at a few of the most significant applications.

Speech Recognition

Audio analysis is central to speech and voice recognition. The voice assistants that 62% of U.S. adults use only work when they can turn audio data into words, and then apply natural language processing (NLP) to understand it.

Distinguishing between voices is another potential use for this niche. Biometric security has gained traction as privacy concerns rise, and voice recognition provides an alternative to face or fingerprint ID.

Anomaly Detection

A/V analysis can also help monitoring solutions identify unusual events. Predictive maintenance sensors, for example, sometimes use audio cues to determine if a machine needs repair based on strange sounds.

Computer vision systems in dashboard cameras can use video anomaly detection to automatically save clips of unsafe behaviors or crashes. A/V editing software could offer AI tools that highlight portions of interest in video or audio files for streamlined workflows.

Security Monitoring

Audio and video anomaly detection is useful for security purposes, too. Open security professional positions are growing at nearly double the average rate, but cameras with recognition and automatic alert features can streamline the work to make up for labor gaps.

AI audio and video systems can alert businesses and homeowners when they detect a potential break-in or other security issue. Some can even automatically contact emergency services for a faster response.

Copyright Enforcement

Alternatively, machine learning professionals could develop A/V detection models to help companies protect their intellectual property. By recognizing unique video or audio signatures, AI can automatically detect copyright infringement and enforce it accordingly.

The core technology behind such systems is similar to tools like Shazam, which identify songs by analyzing their waveforms. Machine vision can perform the same analysis on video content, matching frames and movement to recognize potential copyright issues.

Best Practices in AI Audio and Video Analysis

These four examples just scratch the surface of what audio and video analysis can do in the real world. Those who want to capitalize on this potential should keep a few things in mind to achieve optimal machine learning performance.

Use Optimal Inputs

One crucial but easily missable step is to consider the solution’s input technology. Audio and video files can vary widely and introduce a lot of noise to filter out, but using optimal inputs for the task at hand will improve interpretability.

High-resolution video helps reduce false positives in machine vision. Mono sound channels are the best option for speech intelligibility, so they’re ideal for NLP applications, but stereo inputs will improve copyright detection use cases. Consider the unique advantages and disadvantages of each input type to find what’s right for your goals.

Choose an Appropriate Algorithm

As with all machine learning processes, algorithm selection is also crucial. The best type of model depends on what you want your A/V analysis to accomplish.

Convolutional neural networks offer high accuracy in video analysis but require considerable amounts of data. K-nearest neighbors are sufficient for detecting specific media — like in copyright protection — but less reliable when analyzing a broad range of factors. Keep your input types, goals, computing hardware availability and budget in mind when choosing.

Provide Varied Training Data

Both audio and video data can be hard to classify because they often lack uniform trends. You can account for this by varying your training data. Training the model on a more diverse dataset will help it recognize points of interest in a wider array of real-world situations.

Remember to label and prepare all training data, too, particularly in the early stages before the model gets used to varied data types. There are many user-friendly video editing tools available you can use to adjust some training inputs for clarity and interpretability.

Consider Regulatory Concerns

A/V analysis can also run into unique regulatory issues. Any AI solution that listens to or watches people can introduce privacy concerns. In some cases, these may even include legal restrictions.

Before diving too deep into developing your model, look up any applicable regulations. Be sure you comply with any disclosure or data protection requirements to avoid penalties.

A/V Analysis and Detection Has Many Uses

A/V detection and analysis can serve many different purposes today. It’s useful for both companies and consumers and can be either a serious matter or a simple convenience.

Such a broad market is a promising opportunity for AI developers. Learning how and where you can deploy this technology is the first step in taking advantage of this chance.